Abstract
Semantic web provides information for humans as well as computers to semantically maintain a large-scale of data and provide a meaningful content of unstructured data. It offers new benefits for big-data research and applications. Big data is a new term refers to a massive collection of datasets from various sources in structured, semi-structured, and unstructured data collection. Their integration faces many problems such as the structural and the semantic heterogeneity as the processing of these data is difficult using traditional databases and software techniques. In this paper, the data resources are extracted and aggregated from different sources on the web following by using the geospatial ontology to transform this data into RDF format. RDF format is used to integrate the data semantically and construct the big-data semantic model that is used to store data. The major contribution of this research is to aggregate, integrate, and represent geospatial data semantically. A case study of cities data is used to illustrate the proposed workflow functionalities. The main result of this research is to solve the heterogeneous problem in different data sources with improving the data aggregation, integration, and representation.
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Saber, A., Al-Zoghby, A.M., Elmougy, S. (2018). Big-Data Aggregating, Linking, Integrating and Representing Using Semantic Web Technologies. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_33
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